Pronalaženje varijanti gena iz podataka dobivenih sekvenciranjem

undergraduate thesis

• Title (english): Discovering Gene Variants From Sequencing Data
• Author: Sanja Kosier
• Mentor: Mile Šikić (mentor)
• Committee member: Mile Šikić (predsjednik povjerenstva)
• Committee member: Krešimir Križanović (član povjerenstva)
• Committee member: Josip Marić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2019-07-12, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Aleli predstavljaju dva alternativna gena koja određuju istu osobinu. Pronalazak alela iz višestrukih očitanja gena nije trivijalan. Događa se da su određeni aleli u uzorcima podzastupljeni te je teško programskim rješenjima detektirati njihovo pojavljivanje.
Cilj ovog rada bio je implementirati metode za pronalazak varijanti gena iz uzoraka dobivenih sekvenciranjem. Problem je riješen korištenjem programskih jezika C++ i Python, alata SPOA i Bioparser te algoritmima za poravnanje sekvenci. Razvijene metode čitaju uzorke iz FASTQ datoteka, koriste višestruko poravnanje sekvenci te vrše grupiranje sličnih očitanja u klastere. Nakon toga, odabiru se najveći klasteri na temelju broja očitanja u njima i računa se konsenzusna sekvenca koja naposljetku predstavlja pronađeni alel. U radu su se koristila očitanja MHC gena divokoza.

Abstract (english)

Alleles represent two alternative genes that determine the same feature of an organism. Finding alleles from multiple readings is not trivial. It is possible that certain alleles in the samples are underrepresented and it is difficult to detect their appearance using software solutions.
The aim of this paper was to implement methods for finding gene variants from sequencing data. The problem was solved using C++ and Python programming languages, SPOA and Bioparser tools as well as algorithms for pairwise alignment. Developed methods read samples from FASTQ files, use multiple sequence alignment and perform clustering. Furthermore, the largest clusters are selected based on the number of readings in the clusters and a consensus sequence is generated. A consensus sequence represents a found allele. In this paper the MHC gene samples of chaimos were used.

• Keywords: C++; Python; SPOA; višestruko poravnanje sekvenci; FASTQ; konsenzus; aleli
• Keywords (english): C++; Python; SPOA; multiple sequence alignment; FASTQ; consensus; alleles
• Language: croatian
• URN:NBN: urn:nbn:hr:168:510345
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2020-01-07 19:14:12